Development of a novel autophagy-related gene model for gastric cancer prognostic prediction
Gastric cancer (GC) is a major global health issue and one of the leading causes of tumor-associated mortality worldwide. Autophagy is thought to play a critical role in the development and progression of GC, and this process is controlled by a set of conserved regulators termed autophagy-related ge...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.1006278/full |
| _version_ | 1797996372890222592 |
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| author | Haifeng Xu Haifeng Xu Bing Xu Jiayu Hu Jun Xia Le Tong Ping Zhang Lei Yang Lusheng Tang Sufeng Chen Jing Du Ying Wang Yanchun Li |
| author_facet | Haifeng Xu Haifeng Xu Bing Xu Jiayu Hu Jun Xia Le Tong Ping Zhang Lei Yang Lusheng Tang Sufeng Chen Jing Du Ying Wang Yanchun Li |
| author_sort | Haifeng Xu |
| collection | DOAJ |
| description | Gastric cancer (GC) is a major global health issue and one of the leading causes of tumor-associated mortality worldwide. Autophagy is thought to play a critical role in the development and progression of GC, and this process is controlled by a set of conserved regulators termed autophagy-related genes (ATGs). However, the complex contribution of autophagy to cancers is not completely understood. Accordingly, we aimed to develop a prognostic model based on the specific role of ATGs in GC to improve the prediction of GC outcomes. First, we screened 148 differentially expressed ATGs between GC and normal tissues in The Cancer Genome Atlas (TCGA) cohort. Consensus clustering in these ATGs was performed, and based on that, 343 patients were grouped into two clusters. According to Kaplan–Meier survival analysis, cluster C2 had a worse prognosis than cluster C1. Then, a disease risk model incorporating nine differentially expressed ATGs was constructed based on the least absolute shrinkage and selection operator (LASSO) regression analysis, and the ability of this model to stratify patients into high- and low-risk groups was verified. The predictive value of the model was confirmed using both training and validation cohorts. In addition, the results of functional enrichment analysis suggested that GC risk is correlated with immune status. Moreover, autophagy inhibition increased sensitivity to cisplatin and exacerbated reactive oxygen species accumulation in GC cell lines. Collectively, the results indicated that this novel constructed risk model is an effective and reliable tool for predicting GC outcomes and could help with individual treatment through ATG targeting. |
| first_indexed | 2024-04-11T10:15:18Z |
| format | Article |
| id | doaj.art-b11f460f8d9c445cb3126fe2dd3c63a6 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T10:15:18Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-b11f460f8d9c445cb3126fe2dd3c63a62022-12-22T04:29:58ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-10-011210.3389/fonc.2022.10062781006278Development of a novel autophagy-related gene model for gastric cancer prognostic predictionHaifeng Xu0Haifeng Xu1Bing Xu2Jiayu Hu3Jun Xia4Le Tong5Ping Zhang6Lei Yang7Lusheng Tang8Sufeng Chen9Jing Du10Ying Wang11Yanchun Li12Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaSchool of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, ChinaDepartment of Clinical Laboratory, Hangzhou Women’s Hospital, Hangzhou, ChinaLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaCollege of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United KingdomLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaSchool of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, ChinaLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaLaboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, ChinaDepartment of Central Laboratory, Affiliated Hangzhou first people’s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Central Laboratory, Affiliated Hangzhou first people’s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaGastric cancer (GC) is a major global health issue and one of the leading causes of tumor-associated mortality worldwide. Autophagy is thought to play a critical role in the development and progression of GC, and this process is controlled by a set of conserved regulators termed autophagy-related genes (ATGs). However, the complex contribution of autophagy to cancers is not completely understood. Accordingly, we aimed to develop a prognostic model based on the specific role of ATGs in GC to improve the prediction of GC outcomes. First, we screened 148 differentially expressed ATGs between GC and normal tissues in The Cancer Genome Atlas (TCGA) cohort. Consensus clustering in these ATGs was performed, and based on that, 343 patients were grouped into two clusters. According to Kaplan–Meier survival analysis, cluster C2 had a worse prognosis than cluster C1. Then, a disease risk model incorporating nine differentially expressed ATGs was constructed based on the least absolute shrinkage and selection operator (LASSO) regression analysis, and the ability of this model to stratify patients into high- and low-risk groups was verified. The predictive value of the model was confirmed using both training and validation cohorts. In addition, the results of functional enrichment analysis suggested that GC risk is correlated with immune status. Moreover, autophagy inhibition increased sensitivity to cisplatin and exacerbated reactive oxygen species accumulation in GC cell lines. Collectively, the results indicated that this novel constructed risk model is an effective and reliable tool for predicting GC outcomes and could help with individual treatment through ATG targeting.https://www.frontiersin.org/articles/10.3389/fonc.2022.1006278/fullgastric cancerautophagy-related genesprognostic modeltumor immunitydrug resistance |
| spellingShingle | Haifeng Xu Haifeng Xu Bing Xu Jiayu Hu Jun Xia Le Tong Ping Zhang Lei Yang Lusheng Tang Sufeng Chen Jing Du Ying Wang Yanchun Li Development of a novel autophagy-related gene model for gastric cancer prognostic prediction Frontiers in Oncology gastric cancer autophagy-related genes prognostic model tumor immunity drug resistance |
| title | Development of a novel autophagy-related gene model for gastric cancer prognostic prediction |
| title_full | Development of a novel autophagy-related gene model for gastric cancer prognostic prediction |
| title_fullStr | Development of a novel autophagy-related gene model for gastric cancer prognostic prediction |
| title_full_unstemmed | Development of a novel autophagy-related gene model for gastric cancer prognostic prediction |
| title_short | Development of a novel autophagy-related gene model for gastric cancer prognostic prediction |
| title_sort | development of a novel autophagy related gene model for gastric cancer prognostic prediction |
| topic | gastric cancer autophagy-related genes prognostic model tumor immunity drug resistance |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.1006278/full |
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